All right, stick with me here. Something
huge happened last year that honestly
got way under reportported. We hit what
might just be a critical milestone in
one of the most important arms races our
species has engaged in for the last half
century. And it all has to do with this
compound, Zasserbalpin, which is an
experimental new antibiotic developed by
the Swiss pharma behemoth Ro. Late last
year, Ro announced that phase 1 clinical
trials testing the safety and efficacy
of this new antibiotic were successful
enough to push hispanin straight into a
stage three trial slated for early this
year. Even though that's not a guarantee
this drug will get approved, phase 3 is
still a huge deal considering most new
antibiotics barely make it out of the
stage 1 and two trials. More
importantly, this is the first time a
new class of antibiotic that can
actually kill drugresistant superbugs
has gotten this far in the developmental
process in over 50 years. But also, it's
the way that Zasher Balpin got developed
that's the most exciting. The structure
of Zasural Balpin was refined and built
over multiple years with the assistance
of an old school machine learning
algorithm. Today, I want to use Zasurra
Balpin and its development story as a
lens to show you how quickly the science
of biology is accelerating now that
we're starting to share more and more of
our research burden with computers. Can
we use bots to eradicate our worst bugs?
Let's find out.
And hey, welcome back to Clockwork, a
show about the tiny, beautiful molecules
at the foundation of everything you do.
we've got a lot of ground to cover here, so let's not waste any time. Starting
off with antibiotics in general. Okay,
let's rush the basics. For the purposes
of this video, an antibiotic is any
compound we use to control or kill an
infectious bacteria that's making us
sick. You know, something pathogenic.
This is actually a super hard thing to
pull off chemically if you think about
it. Because for an antibiotic to work,
it has to cause significant harm to one
living thing while not really affecting
the live organism that it's inside of.
Since we're all made out of mostly the
same chemicals, this amounts to a pretty
highstakes game of spot the difference
where we're constantly trying to smash
bacterial machines while leaving our own
component parts untouched. Penicellin is
the classic example here. This beautiful
compound with its iconic betalactum ring
jams up the works in bacterial proteins
like this one which knit a sugary mesh
called pepidoglycin. This is kind of
like the cell wall of gram positive
bacteria. And when penicellin gets stuck
in here and blocks production, these
bacteria will end up exploding once they
try to grow or divide without enough
pepidoglycin for full coverage. Nice.
But that's not the kind of pathogen
we're trying to treat today. We've got
something way worse to deal with. There
are also gram negative bacteria that use
less pepidoglycin and instead rely on a
second cell membrane on top of the
regular one. Getting an antibiotic into
these guys is even harder because now
you have two membranes plus all the
goofy stuff in this parpl to get
through. While we've developed tons of
antibiotics to deal with bacteria of all
types, we haven't built a new class of
antibiotics targeting gram negative
bacteria in 50 years, we've just been
iterating and optimizing around the
handful of antibiotic uh genres we
already have. And I'll get into why that
is a little later. But for now, let's
keep unpacking how bad of a problem that
is first. With very few new enough
weapons in our arsenal, some gram
negative species of bacteria have
managed to evolve truly overwhelming
antibiotic resistance. Catching these
superbugs can become a pretty huge
emergency. And one of the true big bads
of these overpowered pathogens are these
guys which are carbopenum resistant
strains of a species called
asinettobacctor balmani or crab for
short. And asinettobactor are a tough
opponent to deal with even at the base
level. They're ubiquitous opportunistic
little blobs that typically move in and
try to cut out a little territory for
themselves when a person is already
severely ill. They clump together in
these very tough biofilms which are
difficult to clean which allows them to
persist on surfaces for weeks while they
wait to bump into a new host. Their
whole MO is persist and resist. They
fortify their double cell wall with this
hard to penetrate lipopolysaccharide
coating. Even worse, that coating will
explode off of asinettobactor
individuals if they get engulfed by one
of your immune cells as part of a normal
immune response. Once inside, those
lipopolysaccharides, or LPS for short,
by the way, turn out to be super toxic
to immune cells, killing them and
limiting how much of a frontline
response you can mount as this bacteria
set up shop and entrench themselves. And
even worse, if they develop resistance
to an antibiotic called carbopenum,
that's a whole new world of bad news.
Carbopenum is like a streamlined
ultraefficient penicellin. All the
betalactim power with no frills.
Carbopenum is reserved as a last line
antibiotic for severe infections. But
since asinettobacttor bomounti
populations can persist so well, that
means that crab strains tend to
accumulate in places where there are
lots of sick people like hospitals. It's
extremely difficult to treat a crab
infection and they tend to cause sepsis
and severe pneumonia. With treatment
options so limited, crab strains boast a
40 to 60% death rate once they establish
an infection. Globally, crab specific
strains account for a little over 50,000
deaths per year, but they also incur a
significant clinical burden. And the big
concern is that death toll can skyrocket
as resistance spreads. So yeah, these
guys are a priority one global health
emergency, top of the WHO's most wanted
list. And this is why it's so huge that
Zoster Balpin made it this far into
clinical trials. This drug has the
potential to be a beast. But before we
check us out and subscribe today. For now, let's get back to the video. So,
Zashraalpin is a huge deal for two main
reasons. That being how it works and how
it was developed. I'm a molecular
biology sicko at heart. So, let's dive
into how Zaser Balpin kills these crab
fellas first. And right off the bat, do
you notice anything about Zazerbalpin
that stands out compared to the other
antibiotics I've shown so far? I mean
antibiotics are a pretty broad category
from the jump and I haven't shown you
anything super comprehensive but
honestly penicellin is a better
representative of the average antibiotic
weighing in at about 330 dotons with a
dalton basically being just a count of
the number of protons and neutrons in a
molecule. Most antibiotics in the real
world that have real clinical use weigh
in at around 400 doltons with the vast
majority of them keeping things under
600. But Zashra Balpin is a beast by
comparison, weighing in at a few atoms
shy of 800 Daltton. With that size
alone, it's incredible that Zrabalpin
works at all. But I'll try to cover why
when we talk about development. For now,
I'm locking in on mechanism. Let's check
in on how Zasserpin solves our little
crab problem here. So, asinettobactor is
a particularly intense gram negative
fortress to penetrate. Zooming into a
single individual, we've got this
double- layered membrane. And
asinettoiditobacttor is particularly
thorny by adding a whole layer of these
lipopolysaccharides on the outside. And
while this coating gives asintoidobacter
an advantage, we're actually going to
exploit that when zosalpin comes
knocking. These lipopolysaccharides are
an incredible added defense for
asinidobacttor. They help individuals
glue themselves together, block most
medicines outright, and wreck the immune
cells that ingest and destroy infecting
asinidobacttor invaders. But because
this gunk is so well weird, it needs to
be actively pushed to the outer membrane
of these bacteria. And that's going to
be our opening. All across this
parplasmic space, you'll find this
little protein conveyor belt connecting
the two membranes. This is the LPTB2 FGC
complex, but for us, let's just call it
the LPT conveyor belt. The two B
subunits down here use ATP energy to
push lipopolysaccharides into the
central channel, which then moves them
up the bridge and onto the outer
membrane. Now, people have been trying
to target this conveyor belt for ages.
And the underlying theory here is that
if we can block this conveyor belt, then
this LPS goo won't be up top to block
other kinds of antibiotics, allowing
them to get entry. But one thing I want
to point out while we're here, the team
that developed Zuster Balpin wasn't
necessarily trying to target this LPS
conveyor belt. More on that down the
line. Just keep that in the back of your
head for now. And even more importantly,
we're going to see that we don't
necessarily need any other antibiotics
to come in once Balpin has broken the
LPS train. But you guessed it, I'll
explore why in a second. What's
important is Zasherbalpin is going to
act like a kind of molecular glue that
will jam up and break this conveyor
belt. Here's how. Zrabalpin can sneak
past the LPS barrier and bind right in
this pocket as a new lipopolyaccharide
is entering. That chemical bond will be
so absolute that nothing is going to pry
this antibiotic out of this channel.
Basically welding the LPS to the channel
and sealing the whole thing shut. And
there's going to be no more
lipopolysaccharides making it up through
this conveyor belt. We have completely
shut down this transportation mechanism.
Nice. But that's where it gets
interesting because we don't actually
need zostrobelpin as like an opening
move that just drops astobacttor's
shields before we throw out the real
fatal blow. This drug by itself kills
the bacteria outright because it turns
out these bacteria have a completely
separate sensor that tells them when to
stop making that LPS coating. When the
outer shell is full, excess
lipopolysaccharides will overflow and
bump into this protein, which turns off
the LPS factory inside the bacteria.
However, with this conveyor belt turned
off, that switch never gets flipped and
these lipopolysaccharides build up down
here in the bacterial cytoplasm with no
way to get transported out. And you
might remember from before how I said
this stuff is super toxic to our immune
cells. And I mean, yeah, it turns out
we're all made out of the same chemicals
here and a high LPS concentration also
becomes pretty toxic pretty quick for
asinettobactor. So, this LPS will build
up enough to poison acidobacttor
individuals to death with their own cell
wall coding. Nice. Congrats, crabs. You
played yourself. Nice strategy with that
goo barrier. Hope you all enjoy the slow
creep of metabolic collapse. So, in
short, to review, this very large new
class of antibiotic wedges itself into
this bacteria's goo conveyor, which
causes a toxic buildup of
lipopolyaccharides to nuke this punk
from the inside. Awesome. And while this
is all cool and exciting, remember we're
only in phase three trials here. Phase 3
is genuinely a big deal, but it's no
guarantee that Zoster Balpin will clear
the high bar it needs in order to get
real world approval. If it does get
approved, though, the papers that
announced Zoster Balpin and described
its mechanism also managed to identify a
handful of promising new drug binding
targets, which will allow researchers to
expand this new antibiotic category with
more compounds that get out ahead of any
resistance old Krabby here manages to
evolve in the meantime. But that's the
gist of the landscape and a high level
of the mechanism of how Zostravalpin
works. But honestly, that's not the cool
part of the story. I mean, it is to me,
but whatever. That might not even be the
reason you clicked on this video. Sorry
for bearing the lead, by the way. The
really interesting part here is how
Zostraalpin was developed. Like I said
at the beginning, our civilization is
approaching a potential antibiotic
resistance crisis. Bugs like
Asentoacttor have been able to evolve
crab mode because we just haven't been
able to meaningfully expand our
antibiotic arsenal in the last few
decades. But like, why though? It has to
do with how we've gone about discovering
new antibiotic compounds. And I had a
whole long bid explaining that scripted
in this video, but luckily the good
folks at Minute Earth put out a really
solid summary of the problem. Y'all
should check that video out and head
back here if you want a little more
context. In short, we've largely
developed our antibiotics by hunting
down weapons other organisms use to
fight bacteria. And it kind of looks
like we've run out of air quotes natural
antibiotics that we can actually
discover and refine for now. More
context from Minute Earth. Please check
them out. Which makes Oscar Balpin and
its discovery story potentially very
valuable because the research team at RO
that developed this antibiotic did so
with the assistance of an old school
machine learning algorithm. And as a
non-computer guy, I think I need to make
this super clear. This is not generative
AI. I'll get to that towards the end of
this video. Zastra Balpin was discovered
and refined through an old school random
forest machine learning algorithm. Drug
discovery can be slow like that where
we're only seeing real results generated
from the technology of the late 2010s.
But still, how can machine learning
basically pull a new drug like Zrabalpin
out of the ether? Check this out. Again,
Zrabalpin is a massive compound that's
also very hard to pronounce, and I don't
think I'm doing it right. And if not,
it's open season on me in the comments.
I get it. Why did I cover this? Refining
something like this structure without at
least a template from the natural world
would take eons using old school drug
discovery methods. But scientists didn't
just pull this from nothing either. Zra
Balpin is something called a tethered
macrocyclic peptide. to briefly briefly
dip our toes into the deep chemistry
here. Macrocyclic peptides have been a
big research focus for the past 20 years
or so, especially in big pharma and the
biotech scene. You basically take a
chain of amino acids like any protein
and then tether the ends together in a
big old circle. The hope has been that
we can use the diversity inherent in
this kind of structure to build drugs
with the specificity of things like
monoconal antibodies which are gigantic,
but with the deliverability of a much
more compact small molecule drug. You
know, think penicellin. pulling this off
and then developing a platform that
allows you to build specific use MCPS
would be kind of like a mid-tier holy
grail in the pharma world kind of. At
least that's what the press releases
make it sound like. Now, some macroyclic
peptides manage to show some antibiotic
potential in the past. But since we're
talking about peptides here,
theoretically you can mix and match any
number of amino acid links into a
circular chain like this. Meaning there
are billions of potential macrocyclic
peptides we could synthesize. And that's
why we need to deploy computers because
Ro took the shotgun approach to try and
find useful variants of a macroyclic
peptide using a method called wholesale
phenotypic screening. It works like
this. Ro paid a biotech firm to
synthesize a whole bunch of different
kinds of macroyclic peptides. You know,
just shy of 45,000 unique compounds. It
then took those tethered macrocyclic
peptides and tested those against a
bunch of different strains of bacteria.
Just that step alone produced, you
guessed it, a whole mountain of data to
sift through. And this is where machine
learning comes in. Building off of a
framework developed by a team at UC San
Diego back in the day, researchers at RO
scanned every single bacterial strain
tested with each one of those compounds
and fed those images into a machine
learning algorithm that to oversimplify
categorized how each macroyclic peptide
affected those bacterial cells. That
framework from UC San Diego called
cytological profiling allows folks to
identify subtle changes in bacterial
structure and metabolism just from these
small differences in how these red and
blue dyes are processed. which means
that teams can filter out less effective
compounds real quick. The first round of
tests at RO cut the candidate list down
to a single small cluster of similar
compounds with just four having the most
promise. They further refined these
structures by testing which of them
would dissolve better in blood plasma
which resulted in what would eventually
be the structure of Zasra Balpin. Nice.
Nothing like YouTube to synthesize
decades of drug research into like half
a sentence, right? It's awesome. And
yeah, while this section of the video
feels like fan posting about a massive
pharmaceutical company, it's important
to keep in mind that Zasra Balpin is the
culmination of tons of diverse research
projects from all sorts of different
players with publicly funded traditional
research building a foundation that
industry players iterated on top of. And
that's why this is such an exciting
moment in the life sciences. We're
seeing a lot of diverse research paths
with decades of groundwork starting to
cohhere in ways that are making the
discovery process for lots of different
things so much faster. Like sure the
shotgun method Roast used here works
because it's possible to profitably
synthesize tens of thousands of
different macrocyclic peptides for
machine learning assessment but is it
possible to narrow that field before we
start building real chemicals in the
real world? And you know what that's
going to briefly bring us to the new
hotness taking over our economy
generative AI and specifically for us
generative models for building new
antibiotics. I'm going to keep this
brief and focus on only one example, but
there's a lot a lot of development
happening in the life sciences around
generative AI. For me personally,
general use AI models feel more
overhyped than anything, and I haven't
found a single way to make AI fit into
any of my daily workflows. But models
engineered for specific purposes,
especially in chemistry, are starting to
show a lot of promise. In particular,
the James Collins Lab out of MIT has
been producing genuinely incredible
results as they refine their way toward
machine learning powered antibiotic
discovery. At around the same time the
structure and mechanism for Zostrabalin
was announced, the Collins lab published
research tied to an AI model that helped
synthesize another entirely new class of
antibiotic that showed efficacy against
some of the gnarlier strains of
stafylocus arius. That was cool, but
then a few months back they expanded on
that with this monster of a paper
outlining a way more intense method for
developing new antibiotics. I couldn't
be less of a computer guy, so I'm going
to boil this down to the most barebone
details possible. The generative side of
this paper comes in two parts. First,
the team at the Collins lab fed over 45
million possible chemical fragments into
a graph neural network that was testing
for possible efficacy against aphlocous
arius and the bacteria that causes
gorrhea. The results generated from that
neural network were then fed into two
generative algorithms which turned those
promising fragments into full
synthesizable molecules. Those two steps
filtered those 45 million fragments down
to just 24 potential drug candidates,
which the Collins lab then synthesized
and tested against live bacteria. This
is the key distinction that makes me way
more interested in results from
generative AI in the life sciences. Once
you've AI generated a possible drug
candidate or compound or anything, you
can just build it in the real world and
use the old school scientific method to
verify whether it works or not. No
hallucinations, no vaporware, actually
verifiable results. In real life, seven
of those 24 generated structures showed
real world effects on their target
bacteria. But this paper goes into way
more detail about how two of those novel
compounds might represent two entirely
new classes of antibiotic that damage
staff and gorrhea in a unique way. But
we have to keep in mind that at best,
this is the very first step in a very
long process. Like right now, there's
not nearly enough data to see if these
two compounds are actually promising or
not. Verifying if a structure actually
works as a medicine requires just so
much more data before we can start
getting excited and talking about
clinical trials. And heck, even the way
more promising results from Zoster
Balpin can get completely derailed
during phase 3 trials this year. You
rightly have to clear a lot of barriers
in order to prove medicine is safe and
effective before you start saving the
world with a new class of antibiotics.
To hammer it home even harder, Zoster
Balpin is the culmination of a tech tree
that goes all the way back to 2013. None
of these AI generated chemicals are
going to be changing the world tomorrow.
And I mean for real, check back with me
in a decade to see if these things
actually showed real promise, right? And
so the point of this video isn't to
overhype the promise of AI or be a
cheerleader for big pharma's complicated
role in the broader scientific
landscape. Instead, I want you to be
excited for what feels like a truly
revelatory moment in the history of the
life sciences. The last 70 years or so
of molecular biology has just been one
paradigm shift after another. But the
advent of crisper and efficient
sequencing and new imaging methods have
empowered fundamental research teams to
make all sorts of incredible leaps in
our understanding of the base mechanics
of cellular life. Of course, those leaps
have come with a reckoning that
biochemistry operates in ways that are
far more complex and inscrutable than we
initially predicted. But even if the
problems in biology become larger and
thornier, our capacity to break those
problems down and find solutions that
compound on each other appears to be
growing at its own exponential rate. As
best I can, I want to build a window for
you to see all the new wonders coming
out of this incredible moment in
biology. The next few years are going to
be a wild ride as these techniques get
more and more refined, and I'm super
excited to explore all the twists and
turns with you. For now, though, this is
just a good start. I really appreciate
your time. Thanks for exploring all this
with me.
And hey, thanks again for making it all
the way through this video. If you like
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much for being here. For now, real
quick, just let me know what your
favorite planet is in the comments. I
just want to see something here. Either
way, I really appreciate your time.
Thanks so much for being here. As
always, I'd like to leave you with
peace, love, and betalactum rings.
Everyone be well. Thank you so much.